1

Download the 1 km² attribute for the German Census 2011 immigrant rate, which has the German name Auslaender_A. Then download the data for the vacany rate (German name: Leerstandsquote) in the same raster resolution. Make sure to properly define missing values.

The function for downloading is z11::z11_get_1km_attribute() and the one for listing all attributes of this size z11::z11_list_1km_attributes(). There you can also see both of the layer names.

solution 

immigrant_rate_1km <- z11::z11_get_1km_attribute(Auslaender_A)
## Warning in CPL_rasterize(file, driver, st_geometry(sf), values,
## options, : GDAL Message 1: The definition of geographic CRS
## EPSG:4258 got from GeoTIFF keys is not the same as the one from the
## EPSG registry, which may cause issues during reprojection
## operations. Set GTIFF_SRS_SOURCE configuration option to EPSG to use
## official parameters (overriding the ones from GeoTIFF keys), or to
## GEOKEYS to use custom values from GeoTIFF keys and drop the EPSG
## code.
## Warning in CPL_read_gdal(as.character(x), as.character(options),
## as.character(driver), : GDAL Message 1: The definition of geographic
## CRS EPSG:4258 got from GeoTIFF keys is not the same as the one from
## the EPSG registry, which may cause issues during reprojection
## operations. Set GTIFF_SRS_SOURCE configuration option to EPSG to use
## official parameters (overriding the ones from GeoTIFF keys), or to
## GEOKEYS to use custom values from GeoTIFF keys and drop the EPSG
## code.
immigrant_rate_1km[immigrant_rate_1km <= -1] <- NA

vacany_rate_1km <- z11::z11_get_1km_attribute(Leerstandsquote)
## Warning in CPL_rasterize(file, driver, st_geometry(sf), values,
## options, : GDAL Message 1: The definition of geographic CRS
## EPSG:4258 got from GeoTIFF keys is not the same as the one from the
## EPSG registry, which may cause issues during reprojection
## operations. Set GTIFF_SRS_SOURCE configuration option to EPSG to use
## official parameters (overriding the ones from GeoTIFF keys), or to
## GEOKEYS to use custom values from GeoTIFF keys and drop the EPSG
## code.

## Warning in CPL_rasterize(file, driver, st_geometry(sf), values,
## options, : GDAL Message 1: The definition of geographic CRS
## EPSG:4258 got from GeoTIFF keys is not the same as the one from the
## EPSG registry, which may cause issues during reprojection
## operations. Set GTIFF_SRS_SOURCE configuration option to EPSG to use
## official parameters (overriding the ones from GeoTIFF keys), or to
## GEOKEYS to use custom values from GeoTIFF keys and drop the EPSG
## code.
vacany_rate_1km[vacany_rate_1km <= -1] <- NA

immigrant_rate_1km
## class       : SpatRaster 
## dimensions  : 867, 641, 1  (nrow, ncol, nlyr)
## resolution  : 1000, 1000  (x, y)
## extent      : 4031500, 4672500, 2684500, 3551500  (xmin, xmax, ymin, ymax)
## coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
## source(s)   : memory
## name        : lyr.1 
## min value   :     0 
## max value   :   100
vacany_rate_1km
## class       : SpatRaster 
## dimensions  : 867, 641, 1  (nrow, ncol, nlyr)
## resolution  : 1000, 1000  (x, y)
## extent      : 4031500, 4672500, 2684500, 3551500  (xmin, xmax, ymin, ymax)
## coord. ref. : ETRS89-extended / LAEA Europe (EPSG:3035) 
## source(s)   : memory
## name        : lyr.1 
## min value   :     0 
## max value   :   100

2

Crop both datasets to the extent of the city of Berlin.

You can use the osmdata:getbb() function and the argument format_out = "sf_polygon" to receive a proper polygon for this purpose. Make sure only to extract the multipolygon and to convert it to EPSG:3035.

solution 

berlin <- osmdata::getbb("Berlin", format_out = "sf_polygon")

berlin <- 
  berlin$multipolygon |>  
  sf::st_transform(3035)

immigrant_rate_1km_berlin <- terra::crop(immigrant_rate_1km, berlin)

vacancy_rate_1km_berlin <- terra::crop(vacany_rate_1km, berlin)

3

That’s an open exercise: How would you try to conduct a basic correlational analysis of both raster data attributes?

You can coerce the whole raster data into a matrix using terra::as.matrix(x)

solution 

immigrant_rate_1km_berlin_z <- terra::scale(immigrant_rate_1km_berlin)

vacancy_rate_1km_berlin_z <- terra::scale(vacancy_rate_1km_berlin)

cor.test(
  terra::as.matrix(immigrant_rate_1km_berlin_z), 
  terra::as.matrix(vacancy_rate_1km_berlin_z),
  na.rm = TRUE
) 
## 
##  Pearson's product-moment correlation
## 
## data:  terra::as.matrix(immigrant_rate_1km_berlin_z) and terra::as.matrix(vacancy_rate_1km_berlin_z)
## t = 4.1859, df = 1266, p-value = 3.037e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.06218765 0.17079069
## sample estimates:
##       cor 
## 0.1168384
pseudo_heatmap <- immigrant_rate_1km_berlin_z * vacancy_rate_1km_berlin_z

library(tmap)

tmap_arrange(
  tm_shape(immigrant_rate_1km_berlin_z) +
    tm_raster(palette = "-viridis", title = "") +
    tm_layout(title = "Immigrants"),
  tm_shape(vacancy_rate_1km_berlin_z) +
    tm_raster(palette = "-viridis", title = "") +
    tm_layout(title = "Vacancies"),
  tm_shape(pseudo_heatmap) +
    tm_raster(palette = "-viridis", title = "") +
    tm_layout(title = "Immigrants + Vacancies")
)